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Interpretable machine learning for identifying overweight and obesity risk factors of older adults in China
Bozhezi Peng1, Jiani Wu1, Xiaofei Liu2
1School of Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
Objective:
To estimate the importance of risk factors on overweight/obesity among older adults by comparing different predictive model.
Methods:
Survey data from 400 older individuals in China was employed to assess the impacts of four domains of risk factors (demographic, health status, physical activity and neighborhood environment) on overweight/obesity. Six machine learning algorithms were utilized for prediction, and SHapley Additive exPlanations (SHAP) was employed for model interpretation.
Results:
The CatBoost model demonstrated the highest performance among the prediction models for overweight/obesity. Gender, transportation-related physical activity and road network density were top three important features. Other significant factors included falls, cardiovascular conditions, distance to the nearest bus stop and land use mixture.
Conclusion:
Insufficient physical activity, denser road network and incidents of falls increased the likelihood of older adults being overweight/obese. Strategies for preventing overweight/obesity should target transportation-related physical activity, neighborhood environments, and fall prevention specifically.
